Econometrics and Statistics最新文献_第10页

Change point estimation under a copula-based Markov chain model for binomial time series 基于copula的二项时间序列马尔可夫链模型下的变点估计

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.07.007

Takeshi Emura , Ching-Chieh Lai , Li-Hsien Sun

{"title":"Change point estimation under a copula-based Markov chain model for binomial time series","authors":"Takeshi Emura , Ching-Chieh Lai , Li-Hsien Sun","doi":"10.1016/j.ecosta.2021.07.007","DOIUrl":"https://doi.org/10.1016/j.ecosta.2021.07.007","url":null,"abstract":"<div><p><span>Estimation of a change point is a classical statistical problem in sequential analysis and process control. For binomial time series, the existing maximum likelihood estimators<span> (MLEs) for a change point are limited to independent observations. If the independence assumption is violated, the MLEs substantially lose their efficiency, and a likelihood function provides a poor fit to the data. A novel change point estimator<span> is proposed under a copula-based Markov chain model for serially dependent observations. The main novelty is the adaptation of a three-state </span></span></span>copula<span> model, consisting of the in-control state, out-of-control state, and transition state. Under this model, a MLE is proposed with the aid of profile likelihood. A parametric bootstrap method is adopted to compute a confidence set for the unknown change point. The simulation studies show that the proposed MLE is more efficient than the existing estimators when serial dependence in observations are specified by the model. The proposed method is illustrated by the jewelry manufacturing data, where the proposed model gives an improved fit.</span></p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"28 ","pages":"Pages 120-137"},"PeriodicalIF":1.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ecosta.2021.07.007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50198795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Partially orthogonal blocked three-level response surface designs 部分正交阻塞三电平响应面设计

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.08.007

Heiko Großmann , Steven G. Gilmour

引用次数: 3

Robust nonparametric multiple changepoint detection for multivariate variability 多变量变异性的鲁棒非参数多变化点检测

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2023.09.001

Kelly Ramsay, Shojaeddin Chenouri

{"title":"Robust nonparametric multiple changepoint detection for multivariate variability","authors":"Kelly Ramsay, Shojaeddin Chenouri","doi":"10.1016/j.ecosta.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.09.001","url":null,"abstract":"Two robust, nonparametric multiple changepoint detection algorithms are introduced: DWBS and MKWP. These algorithms can detect multiple changes in the variability of a sequence of independent multivariate observations, even when the number of changepoints is unknown. The algorithms DWBS and MKWP require minimal distributional assumptions and are robust to outlying observations and heavy tails. The DWBS algorithm uses a local search method based on depth-based ranks and wild binary segmentation. The MKWP algorithm estimates changepoints globally via maximizing a penalized version of the classical Kruskal–Wallis ANOVA test statistic. It is demonstrated that this objective function can be maximized via the well-known PELT algorithm. Under mild, nonparametric assumptions, both of these algorithms are shown to be consistent for the correct number of changepoints and the correct location(s) of the changepoint(s). A data driven thresholding method for multivariate data is introduced, based on the Schwartz information criteria. The robustness and accuracy of the new methods is demonstrated with a simulation study, where the algorithms are compared to several existing algorithms. These new methods can estimate the number of changepoints and their locations accurately when the data are heavy tailed or skewed and the sample size is large. Lastly, the proposed algorithms are applied to a four-dimensional sequence of European daily stock returns.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134936298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vine Copula based Portfolio Level Conditional Risk Measure Forecasting 基于Vine Copula的组合水平条件风险度量预测

Econometrics and Statistics Pub Date : 2023-08-01 DOI: 10.1016/j.ecosta.2023.08.002

Emanuel Sommer, Karoline Bax, Claudia Czado

{"title":"Vine Copula based Portfolio Level Conditional Risk Measure Forecasting","authors":"Emanuel Sommer, Karoline Bax, Claudia Czado","doi":"10.1016/j.ecosta.2023.08.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.08.002","url":null,"abstract":"Accurately estimating risk measures for financial portfolios and validating their robustness is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level, hence they fail to capture the complex cross-dependencies between portfolio components and particularly provide no methodology to perform a sensitivity analysis on the estimates. To address both aspects, a new approach is presented that uses vine copulas in combination with univariate ARMA-GARCH models for marginal modelling to compute conditional portfolio-level risk measure estimates by simulating portfolio-level forecasts conditioned on a stress factor. A quantile-based approach is then presented to observe the behaviour of risk measures given a particular state of the conditioning asset(s). In an illustrative case study of Spanish equities with different stress factors, the results show that the portfolio is quite robust to a sharp downturn in the American market. At the same time, there is no evidence of this behaviour with respect to the European market. The novel algorithms presented are ready for use through the R package portvine, which is publicly available on CRAN.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136221687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Computationally Efficient Mixture Innovation Model for Time-Varying Parameter Regressions 一个计算效率高的时变参数回归混合创新模型

IF 1.9

Econometrics and Statistics Pub Date : 2023-08-01 DOI: 10.1016/j.ecosta.2023.08.001

Zhongfang He

引用次数: 0

Seasonality in High Frequency Time Series 高频时间序列的季节性

IF 1.9

Econometrics and Statistics Pub Date : 2023-07-01 DOI: 10.1016/j.ecosta.2022.02.001

Tommaso Proietti , Diego J. Pedregal

引用次数: 0

Macroeconomic Uncertainty and Vector Autoregressions 宏观经济不确定性与向量自回归

IF 1.9

Econometrics and Statistics Pub Date : 2023-07-01 DOI: 10.1016/j.ecosta.2023.07.002

Mario Forni, Luca Gambetti, Luca Sala

引用次数: 2

A Weissman-type estimator of the conditional marginal expected shortfall 条件边际期望短缺的Weissman型估计

IF 1.9

Econometrics and Statistics Pub Date : 2023-07-01 DOI: 10.1016/j.ecosta.2021.09.006

Yuri Goegebeur , Armelle Guillou , Nguyen Khanh Le Ho , Jing Qin

引用次数: 5

A Multivariate Randomized Response Model for Sensitive Binary Data 敏感二元数据的多变量随机响应模型

IF 1.9

Econometrics and Statistics Pub Date : 2023-07-01 DOI: 10.1016/j.ecosta.2022.01.003

Amanda M.Y. Chu , Yasuhiro Omori , Hing-yu So , Mike K.P. So

{"title":"A Multivariate Randomized Response Model for Sensitive Binary Data","authors":"Amanda M.Y. Chu , Yasuhiro Omori , Hing-yu So , Mike K.P. So","doi":"10.1016/j.ecosta.2022.01.003","DOIUrl":"https://doi.org/10.1016/j.ecosta.2022.01.003","url":null,"abstract":"<div><p><span>A new statistical method is proposed to combine the randomized response technique, probit modeling, and </span>Bayesian analysis<span> to analyze large-scale online surveys of multiple binary randomized responses. The proposed method is illustrated by analyzing sensitive dichotomous randomized responses on different types of drug administration error from nurses in a hospital cluster. A statistical challenge is that nurses’ true sensitive responses are unobservable because of a randomization scheme that protects their data privacy to answer the sensitive questions. Four main contributions of the paper are highlighted. The first is the construction of a generic statistical approach in modeling multivariate sensitive binary data collected from the randomized response technique. The second is studying the dependence of multivariate sensitive responses via statistical measures. The third is the calculation of an overall attitude score using sensitive responses. The last one is an illustration of the proposed statistical method for analyzing administration policies that potentially involve sensitive topics which are important to study but are not easily investigated via empirical studies. The particular healthcare example on drug administration policies demonstrated in this paper also presents a scientific way to elicit managerial strategies while protecting data privacy through analytics.</span></p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"27 ","pages":"Pages 16-35"},"PeriodicalIF":1.9,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50178657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of Extreme Risk Measures for Stochastic Volatility Models with Long Memory and Heavy Tails 长记忆重尾随机波动模型的极端风险测度估计

IF 1.9

Econometrics and Statistics Pub Date : 2023-07-01 DOI: 10.1016/j.ecosta.2023.07.004

Clémonell Bilayi-Biakana, G. Ivanoff, Rafal Kulik

引用次数: 1